Date of Award

2021-05-01

Degree Name

Master of Science

Department

Computer Science

Advisor(s)

Monika Akbar

Abstract

Researchers have worked on modeling and predicting the likelihood of developingchronic diseases, such as diabetes and high blood pressure, using medical data (e.g., heart-rate, blood sugar). However, many of these diseases demonstrate strong links with demographics and socio-economic status (e.g., race, gender, income). It is also less time-consuming to retrieve demographic and socio-economic data, some of which are publicly available through US Census Bureau, than to carry out medical tests. Hence, demographic data can give a quicker estimate of the susceptibility of a person to a chronic disease.

In this work, we study the effect of using medical vs. demographics data formodelling and predicting two chronic diseases: diabetes and high blood pressure. We proposed an updated deprivation index to build disease models that consider demographic data. Our results indicate demographic data are as good or better indicators for predicting chronic diseases.

Language

en

Provenance

Recieved from ProQuest

File Size

93 p.

File Format

application/pdf

Rights Holder

OLUGBENGA TEMITOPE IYIOLA

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